In a biomedical cohort study for assessing the association between an outcome variable and a set of covariates, it is common that some covariates can only be measured on a subgroup of study subjects. An important design question is which subjects to select into the subgroup towards increased statistical efficiency. When the outcome is binary, one may adopt a case-control sampling design or a balanced case-control design where cases and controls are further matched on a small number of complete discrete covariates. While the latter achieves success in estimating odds ratio (OR) parameters for the matching covariates, similar two-phase design options have not been explored for the remaining covariates, especially the incompletely collected ones. This is of great importance in studies where the covariates of interest cannot be completely collected. To this end, assuming that an external model is available relating the outcome and complete covariates, we propose a novel sampling scheme that oversamples cases and controls with worse goodness-of-fit based on the external model and further matches them on complete covariates similarly to the balanced design. We develop a pseudolikelihood method for estimating OR parameters. Through simulation studies and explorations in a real cohort study, we find that our design generally leads to reduced asymptotic variances of the OR estimates and the reduction for the matching covariates is comparable to that of the balanced design. This article is protected by copyright. All rights reserved.

中文翻译：

---

用于研究二元结果的新型两阶段抽样设计


在评估结果变量与一组协变量之间的关联的生物医学队列研究中，通常某些协变量只能在研究对象的子组上进行测量。一个重要的设计问题是选择哪些受试者进入子组以提高统计效率。当结果是二元时，可以采用病例对照抽样设计或平衡病例对照设计，其中病例和对照在少量完整离散协变量上进一步匹配。虽然后者在估计匹配协变量的优势比 (OR) 参数方面取得了成功，但尚未针对其余协变量（尤其是不完全收集的协变量）探索类似的两阶段设计选项。这对于无法完全收集感兴趣的协变量的研究非常重要。为此，假设有一个与结果和完整协变量相关的外部模型，我们提出了一种新颖的采样方案，该方案基于外部模型对拟合优度较差的案例和控件进行过采样，并进一步将它们与完整协变量进行匹配，类似于平衡的设计。我们开发了一种用于估计 OR 参数的伪似然方法。通过模拟研究和真实队列研究的探索，我们发现我们的设计通常会减少 OR 估计的渐近方差，并且匹配协变量的减少与平衡设计相当。本文受版权保护。版权所有。